Bayesian History Matching applied to the calibration of a gravity wave parameterization

Abstract

Breaking atmospheric gravity waves in the tropical stratosphere are essential in driving the roughly two year oscillation of zonal winds in this region known as the Quasi-Biennial Oscillation (QBO). As Global Climate Models (GCM)s are not typically able to directly resolve the spectrum of waves required to drive the QBO, parameterizations are necessary. Such parameterizations often require knowledge of poorly constrained physical parameters. In the case of the spectral gravity parameterization used in this work, these parameters are the total equatorial gravity wave stress and the half width of phase speed distribution. Radiosonde observations are used to obtain the period and amplitude of the QBO, which are compared against values obtained from a GCM. We utilize two established calibration techniques to obtain estimates of the range of plausible parameter values: History Matching & Ensemble Kalman Inversion (EKI). History Matching is found to reduce the size of the initial range of plausible parameters by a factor of 98%, requiring only 60 model integrations. EKI cannot natively provide any uncertainty quantification but is able to produce a single best estimate of the calibrated values in 25 integrations. When directly comparing the approaches using the Calibrate, Emulate, Sample method to produce a posterior estimate from EKI, History Matching produces more compact posteriors with fewer model integrations at lower ensemble sizes compared to EKI; however, these differences become less apparent at higher ensemble sizes.

CalibrateEmulateSample.jl: Accelerated Parametric Uncertainty Quantification

“…Some research projects that use this codebase, or modifications of it, are • (Dunbar et al, 2021) • (Bieli et al, 2022) • (Hillier, 2022) • (Howland et al, 2022) • (Dunbar, Howland, et al, 2022) • (Mansfield & Sheshadri, 2022) • (King et al, 2023)…”